Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1076465 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27962963_1.fastq.gz | Fastqsanger.gz | |||
| SRR27962963_2.fastq.gz | Fastqsanger.gz | |||
| SRR27962964_1.fastq.gz | Fastqsanger.gz | |||
| SRR27962964_2.fastq.gz | Fastqsanger.gz | |||
| SRR27962965_1.fastq.gz | Fastqsanger.gz |
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Project description:Hepatic molecular adaptations underlying periparturient metabolic diseases such as ketosis in dairy cows are largely unknown. We used a simple model for induction of ketosis to examine liver gene expression profiles using a microarray consisting of 13,257 annotated cattle oligonucleotides. At 4 days post-partum, 7 cows classified as healthy after a physical examination were fed at 50% of intake at day 4 from day 5 to signs of ketosis or until 14 days post-partum. Another group of 7 healthy cows served as controls. Liver was biopsied at 9-14 (ketosis) or 14 days post-partum (controls). More than 9,000 sequences represented on the microarray were expressed in liver. Keywords: disease state analysis
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Project description:Bovine mastitis, the infection of the mammary gland which leads to great health and economic challenges for dairy farmers is accompanied by dramatic changes in the milk proteome. In this study of naturally occurring mastitis not only have the changes in the milk proteome been quantified in subclinical and clinical mastitis but simultaneous changes in the serum proteome have also been characterised and quantified. Milk and serum samples from healthy dairy cows (n=12) were compared to those of cows with subclinical (n=10) and clinical mastitis (n=112) using TMT label-based proteomic approach. The study included the milk and serum samples taken from thirty-two dairy cows ( kept on private farms located in Croatia. All cows were checked by physical examination. Somatic cells count (SCC) and mastitis test in milk samples were performed. According to the results, cows were assigned into three groups: Group I (control, n=10) consisted of healthy cows with SCC below 400,000 cells/ml on the monthly check-up and a negative mastitis test and without any clinical sign of mastitis. Group II (subclinical mastitis, n=12) comprised cows without clinical signs of mastitis but with SCC above 400,000 cells/ml on the monthly basis and a positive mastitis test at the time of sampling. Group III (clinical mastitis, n=10) consisted of cows with clinical signs of mastitis which include changes in milk appearance (flakes and clots in milk), different stages of udder inflammation (hyperemia, edema, pain, udder enlargement and elevated udder temperature) and disturbance of general health (depression, relaxed cold ears, dehydration, elevated body temperature, increased heart and respiratory rate, decreased ruminal contraction and decreased appetite). Blood samples were taken from v. coccygea and centrifuged at 3000 g for 15 min after clotting for two hours at room temperature. Serum samples were stored at -80°C until analysis. Milk samples were taken aseptically before the morning milking. First few streams were discarded. Teat ends were disinfected with cotton swabs soaked with 70% ethanol. Samples were taken into sterile tubes and transported to laboratory on ice within a few hours.
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Project description:M. Berg, J. Plöntzke, S. Leonhard-Marek, K.E. Müller & S. Röblitz. A dynamic model to simulate potassium balance in dairy cows. Journal of Dairy Science 100, 12 (2017).
High-performing dairy cows require a particular composition of nutritional ingredients, adapted to their individual requirements and depending on their production status. The optimal dimensioning of minerals in the diet, one being potassium, is indispensable for the prevention of imbalances. Potassium balance in cows is the result of potassium intake, distribution in the organism, and excretion, and it is closely related to glucose and electrolyte metabolism. In this paper, we present a dynamical model for potassium balance in lactating and nonlactating dairy cows based on ordinary differential equations. Parameter values were obtained from clinical trial data and from the literature. To verify the consistency of the model, we present simulation outcomes for 3 different scenarios: potassium balance in (1) nonlactating cows with varying feed intake, (2) nonlactating cows with varying potassium fraction in the diet, and (3) lactating cows with varying milk production levels. The results give insights into the short- and long-term potassium metabolism, providing an important step toward the understanding of the potassium network, the design of prophylactic feed additives, and possible treatment strategies.
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